Application of Large Language Model Methods in Scientific and Tech-nical Intelligence Practice
[Purpose/significance]With the strong ability to process large-scale datasets and outstanding performance in various natural language processing tasks,large language models(LLMs)have excelled across multiple industries.Since scientific and technical intelligence primarily relies on textual data,LLMs are naturally well-suited for this field,ushering in a new wave of transformative changes.[Method/process]This article discusses the advantages of LLMs from five perspectives:low-dimensional dense vector representations of text,large-scale pre-trained models,fine-tuning and prompt learning,high-quality large-scale training data,and human alignment techniques.[Result/conclusion]LLMs have extensive applications in tasks such as intelligence identification,intelligence tracking,intelli-gence evaluation,and intelligence prediction,resulting in significant optimization improvements or paradigm shifts.
large language modelscientific and technical intelligenceintelligence methodintelligence practicedeep learningtextual information
万方数据
维普
